from model import Model
from pySAXS.LS.LSsca import Qlogspace
from pySAXS.LS.LSsca import getV
from pySAXS.LS.LSsca import P1
import numpy
[docs]class PolyGauss(Model):
'''
class monoSphere from LSSca
for Spheres poly-Gauss Model
by OT 10/06/2009
'''
[docs] def PolyGauss_anaFunction(self,q,par):
"""
q array of q (A-1)
par[0] Mean radius(A)
par[1] FWHM (A)
par[2] concentration of sphere (cm-3)
par[3] scattering length density of sphere (cm-2)
par[4] scattering length density of outside (cm-2)
"""
a=par[0]
s=par[1]/(2.*a*(numpy.log(2.))**0.5 )
t1=q*a*s
t2=2.*q*a
t3=q*a
f1=1.+ (1.+0.5*s**2.)*((t3)**2.) - (t2)*( 1.+(t1**2.) )*numpy.sin(t2)*numpy.exp(-t1**2.) - ( 1.+(1.5*(s**2.)-1.)*(t3**2.) + (t1**4.) )*numpy.cos(t2)*numpy.exp(-(t1**2.))
f2=4.5*(t3**(-6.))*(1.+7.5*s**2.+(45./4.)*s**4.+(15./8.)*s**6.)**(-1)
normfactor=((4.*(a**3.)*numpy.pi/3.)**2.)*(1.+7.5*(s**2.)+(45./4.)*(s**4.)+(15./8.)*(s**6.))
return normfactor*1e-48*par[2]*((par[3]-par[4])**2.)*f1*f2
'''
parameters definition
Model(1,PolyGauss_ana,
Qlogspace(1e-4,1.,500.)
([250.,10.,1.5e14,2e11,1e10]),
("Mean (A)",
"Polydispersity ",
"number density",
"scattering length density of sphere (cm-2)",
"scattering length density of medium (cm-2)"
),("%f","%f","%1.3e","%1.3e","%1.3e")
,(True,True,False,False,False)),
from LSsca
'''
IntensityFunc=PolyGauss_anaFunction #function
N=0
q=Qlogspace(1e-4,1.,500.) #q range(x scale)
Arg=[250.,10.,1.5e14,2e11,1e10] #list of defaults parameters
Format=["%f","%f","%1.3e","%1.3e","%1.3e"] #list of c format
istofit=[True,True,True,True,True] #list of boolean for fitting
name="Spheres poly-Gauss" #name of the model
Doc=["Mean (A)",\
"Polydispersity ",\
"number density",\
"scattering length density of sphere (cm-2)",\
"scattering length density of medium (cm-2)"] #list of description for parameters
if __name__=="__main__":
'''
test code
'''
modl=PolyGauss()
#plot the model
import Gnuplot
gp=Gnuplot.Gnuplot()
gp("set logscale xy")
c=Gnuplot.Data(modl.q,modl.getIntensity(),with_='points')
gp.plot(c)
raw_input("enter")
#plot and fit the noisy model
yn=modl.getNoisy(0.4)
cn=Gnuplot.Data(modl.q,yn,with_='points')
res=modl.fit(yn)
cf=Gnuplot.Data(modl.q,modl.IntensityFunc(modl.q,res),with_='lines')
gp.plot(c,cn,cf)
raw_input("enter")
#plot and fit the noisy model with fitBounds
bounds=modl.getBoundsFromParam() #[250.0,2e11,1e10,1.5e15]
res2=modl.fitBounds(yn,bounds)
print res2
raw_input("enter")